Keywords: Hyperpolarized MR (Gas), Hyperpolarized MR (Gas)
Motivation: Hyperpolarized 129Xe MRI, pivotal for lung function analysis, faces challenges in standardizing ventilation distribution calculations, particularly regarding image rescaling and major airway inclusion.
Goal(s): To develop a deep learning-based method to segment the trachea and assess its impact on the ventilation distribution.
Approach: We trained and compared various deep learning models for robust segmentation.
Results: In patients with interstitial lung disease, the ratio of 129Xe signal in the trachea versus the distal lung is 2.5-fold higher than in healthy volunteers. If such signal is not segmented out before histogram rescaling, ventilation distributions may be substantially skewed in patients with restrictive disease.
Impact: This research paves the way for redefining the calculation of ventilation distribution in pulmonary imaging by incorporating large airways like the trachea, potentially leading to more precise imaging metrics and improved clinical outcomes for pulmonary diseases.
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